

import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
__snake_case				:      Union[str, Any]   		=			logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt""")



@dataclass
class        A__:
						"""simple docstring"""






						_A    :				Optional[str]       					=			field(
						    default='''cifar10''',  metadata={'''help''': '''Name of a dataset from the datasets package'''}							)
						_A    :				Optional[str]       					=			field(
						    default=a_,  metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}							)
						_A    :				Optional[str]       					=			field(
						    default=a_,  metadata={'''help''': '''The column name of the images in the files.'''}							)
						_A    :				Optional[str]       					=			field(default=a_,  metadata={'''help''': '''A folder containing the training data.'''}							)
						_A    :				Optional[str]       					=			field(default=a_,  metadata={'''help''': '''A folder containing the validation data.'''}							)
						_A    :				Optional[float]       					=			field(
						    default=0.15,  metadata={'''help''': '''Percent to split off of train for validation.'''}							)
						_A    :				Optional[int]       					=			field(
						    default=a_,  metadata={
						        '''help''': (
						            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
						            '''value if set.'''
						        )
						    },  )
						_A    :				Optional[int]       					=			field(
						    default=a_,  metadata={
						        '''help''': (
						            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
						            '''value if set.'''
						        )
						    },  )





						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									a_						:						List[str] 							= {}
									if self.train_dir is not None:
												a_						:						List[str] 							= self.train_dir
									if self.validation_dir is not None:
												a_						:						Optional[int] 							= self.validation_dir
									a_						:						Union[str, Any] 							= data_files if data_files else None







@dataclass
class        A__:
						"""simple docstring"""






						_A    :				str       					=			field(
						    default=a_,  metadata={
						        '''help''': (
						            '''The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'''
						        )
						    },  )
						_A    :				Optional[str]       					=			field(
						    default=a_,  metadata={'''help''': '''Pretrained config name or path if not the same as model_name_or_path'''}							)
						_A    :				Optional[str]       					=			field(
						    default=a_,  metadata={
						        '''help''': (
						            '''Override some existing default config settings when a model is trained from scratch. Example: '''
						            '''n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'''
						        )
						    },  )
						_A    :				Optional[str]       					=			field(
						    default=a_,  metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''}							)
						_A    :				str       					=			field(
						    default='''main''',  metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''},  )
						_A    :				str       					=			field(default=a_,  metadata={'''help''': '''Name or path of preprocessor config.'''}							)
						_A    :				bool       					=			field(
						    default=a_,  metadata={
						        '''help''': (
						            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
						            '''with private models).'''
						        )
						    },  )
						_A    :				float       					=			field(
						    default=0.75,  metadata={'''help''': '''The ratio of the number of masked tokens in the input sequence.'''}							)
						_A    :				bool       					=			field(
						    default=a_,  metadata={'''help''': '''Whether or not to train with normalized pixel values as target.'''}							)







@dataclass
class        A__(a_							):
						"""simple docstring"""






						_A    :				float       					=			field(
						    default=1E-3,  metadata={'''help''': '''Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'''}							)







def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Optional[Any] 							= torch.stack([example["""pixel_values"""] for example in examples])
			return {"pixel_values": pixel_values}


def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			a_						:						Any 							= HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments))
			if len(sys.argv) == 2 and sys.argv[1].endswith(""".json"""):
						# If we pass only one argument to the script and it's the path to a json file,
						# let's parse it to get our arguments.
						a_							,					a_							,					a_						:						List[str] 							= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
			else:
						a_							,					a_							,					a_						:						Optional[int] 							= parser.parse_args_into_dataclasses()

			# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
			# information sent is the one passed as arguments along with your Python/PyTorch versions.
			send_example_telemetry("""run_mae"""  ,	a__  ,	a__)

			# Setup logging
			logging.basicConfig(
			    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""  ,	datefmt="""%m/%d/%Y %H:%M:%S"""  ,	handlers=[logging.StreamHandler(sys.stdout)]  ,	)

			if training_args.should_log:
						# The default of training_args.log_level is passive, so we set log level at info here to have that default.
						transformers.utils.logging.set_verbosity_info()

			a_						:						Optional[int] 							= training_args.get_process_log_level()
			logger.setLevel(a__)
			transformers.utils.logging.set_verbosity(a__)
			transformers.utils.logging.enable_default_handler()
			transformers.utils.logging.enable_explicit_format()

			# Log on each process the small summary:
			logger.warning(
			    f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
			    + f'''distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fpaa}''')
			logger.info(f'''Training/evaluation parameters {training_args}''')

			# Detecting last checkpoint.
			a_						:						List[str] 							= None
			if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
						a_						:						Optional[int] 							= get_last_checkpoint(training_args.output_dir)
						if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
									raise ValueError(
									    f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
									    """Use --overwrite_output_dir to overcome.""")
						elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
									logger.info(
									    f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
									    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""")

    # Initialize our dataset.
			a_						:						str 							= load_dataset(
			    data_args.dataset_name  ,	data_args.dataset_config_name  ,	data_files=data_args.data_files  ,	cache_dir=model_args.cache_dir  ,	use_auth_token=True if model_args.use_auth_token else None  ,	)

			# If we don't have a validation split, split off a percentage of train as validation.
			a_						:						List[str] 							= None if """validation""" in ds.keys() else data_args.train_val_split
			if isinstance(data_args.train_val_split  ,	a__) and data_args.train_val_split > 0.0:
						a_						:						int 							= ds["""train"""].train_test_split(data_args.train_val_split)
						a_						:						int 							= split["""train"""]
						a_						:						Tuple 							= split["""test"""]

			# Load pretrained model and image processor
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.
			a_						:						Union[str, Any] 							= {
			    """cache_dir""": model_args.cache_dir,
			    """revision""": model_args.model_revision,
			    """use_auth_token""": True if model_args.use_auth_token else None,
			}
			if model_args.config_name:
						a_						:						List[Any] 							= ViTMAEConfig.from_pretrained(model_args.config_name  ,	**a__)
			elif model_args.model_name_or_path:
						a_						:						Optional[int] 							= ViTMAEConfig.from_pretrained(model_args.model_name_or_path  ,	**a__)
			else:
						a_						:						Dict 							= ViTMAEConfig()
						logger.warning("""You are instantiating a new config instance from scratch.""")
						if model_args.config_overrides is not None:
									logger.info(f'''Overriding config: {model_args.config_overrides}''')
									config.update_from_string(model_args.config_overrides)
									logger.info(f'''New config: {config}''')

    # adapt config
			config.update(
			    {
			        """mask_ratio""": model_args.mask_ratio,
			        """norm_pix_loss""": model_args.norm_pix_loss,
			    })

			# create image processor
			if model_args.image_processor_name:
						a_						:						int 							= ViTImageProcessor.from_pretrained(model_args.image_processor_name  ,	**a__)
			elif model_args.model_name_or_path:
						a_						:						Union[str, Any] 							= ViTImageProcessor.from_pretrained(model_args.model_name_or_path  ,	**a__)
			else:
						a_						:						Optional[Any] 							= ViTImageProcessor()

			# create model
			if model_args.model_name_or_path:
						a_						:						Tuple 							= ViTMAEForPreTraining.from_pretrained(
						    model_args.model_name_or_path  ,	from_tf=bool(""".ckpt""" in model_args.model_name_or_path)  ,	config=a__  ,	cache_dir=model_args.cache_dir  ,	revision=model_args.model_revision  ,	use_auth_token=True if model_args.use_auth_token else None  ,	)
			else:
						logger.info("""Training new model from scratch""")
						a_						:						Optional[int] 							= ViTMAEForPreTraining(a__)

			if training_args.do_train:
						a_						:						List[Any] 							= ds["""train"""].column_names
			else:
						a_						:						Union[str, Any] 							= ds["""validation"""].column_names

			if data_args.image_column_name is not None:
						a_						:						Optional[int] 							= data_args.image_column_name
			elif "image" in column_names:
						a_						:						List[Any] 							= """image"""
			elif "img" in column_names:
						a_						:						Union[str, Any] 							= """img"""
			else:
						a_						:						int 							= column_names[0]

			# transformations as done in original MAE paper
			# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
			if "shortest_edge" in image_processor.size:
						a_						:						Optional[int] 							= image_processor.size["""shortest_edge"""]
			else:
						a_						:						Dict 							= (image_processor.size["""height"""], image_processor.size["""width"""])
			a_						:						str 							= Compose(
			    [
			        Lambda(lambda a__: img.convert("""RGB""") if img.mode != "RGB" else img),
			        RandomResizedCrop(a__  ,	scale=(0.2, 1.0)  ,	interpolation=InterpolationMode.BICUBIC),
			        RandomHorizontalFlip(),
			        ToTensor(),
			        Normalize(mean=image_processor.image_mean  ,	std=image_processor.image_std),
			    ])

			def preprocess_images(a__):
						a_						:						str 							= [transforms(a__) for image in examples[image_column_name]]
						return examples

			if training_args.do_train:
						if "train" not in ds:
									raise ValueError("""--do_train requires a train dataset""")
						if data_args.max_train_samples is not None:
									a_						:						Optional[int] 							= ds["""train"""].shuffle(seed=training_args.seed).select(range(data_args.max_train_samples))
						# Set the training transforms
						ds["train"].set_transform(a__)

			if training_args.do_eval:
						if "validation" not in ds:
									raise ValueError("""--do_eval requires a validation dataset""")
						if data_args.max_eval_samples is not None:
									a_						:						Any 							= (
									    ds["""validation"""].shuffle(seed=training_args.seed).select(range(data_args.max_eval_samples))
									)
						# Set the validation transforms
						ds["validation"].set_transform(a__)

			# Compute absolute learning rate
			a_						:						Optional[int] 							= (
			    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
			)
			if training_args.base_learning_rate is not None:
						a_						:						int 							= training_args.base_learning_rate * total_train_batch_size / 2_5_6

			# Initialize our trainer
			a_						:						Any 							= Trainer(
			    model=a__  ,	args=a__  ,	train_dataset=ds["""train"""] if training_args.do_train else None  ,	eval_dataset=ds["""validation"""] if training_args.do_eval else None  ,	tokenizer=a__  ,	data_collator=a__  ,	)

			# Training
			if training_args.do_train:
						a_						:						Optional[int] 							= None
						if training_args.resume_from_checkpoint is not None:
									a_						:						Any 							= training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									a_						:						Dict 							= last_checkpoint
						a_						:						int 							= trainer.train(resume_from_checkpoint=a__)
						trainer.save_model()
						trainer.log_metrics("""train"""  ,	train_result.metrics)
						trainer.save_metrics("""train"""  ,	train_result.metrics)
						trainer.save_state()

			# Evaluation
			if training_args.do_eval:
						a_						:						Union[str, Any] 							= trainer.evaluate()
						trainer.log_metrics("""eval"""  ,	a__)
						trainer.save_metrics("""eval"""  ,	a__)

			# Write model card and (optionally) push to hub
			a_						:						int 							= {
			    """tasks""": """masked-auto-encoding""",
			    """dataset""": data_args.dataset_name,
			    """tags""": ["""masked-auto-encoding"""],
			}
			if training_args.push_to_hub:
						trainer.push_to_hub(**a__)
			else:
						trainer.create_model_card(**a__)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			main()


if __name__ == "__main__":
	main()





import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__snake_case				:      Union[str, Any]   		=			logging.getLogger(__name__)



@dataclass
class        A__(a_							):
						"""simple docstring"""






						_A    :				Optional[float]       					=			field(
						    default=0.0,  metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''}							)
						_A    :				bool       					=			field(default=a_,  metadata={'''help''': '''Whether to SortishSamler or not.'''}							)
						_A    :				bool       					=			field(
						    default=a_,  metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}							)
						_A    :				bool       					=			field(default=a_,  metadata={'''help''': '''whether to use adafactor'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(default=a_,  metadata={'''help''': '''Dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[float]       					=			field(
						    default=a_,  metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''}							)
						_A    :				Optional[str]       					=			field(
						    default='''linear''',  metadata={'''help''': f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()							)}'''},  )


